The differentiation of human pluripotent stem cells into cardiomyocytes is now relatively commonplace, and a heterogeneous mixture of electrophysiological phenotypes is generally obtained. The relative proportions of the 3 major phenotypes (nodal-like, atrial-like and ventricular-like) have been widely reported for various differentiation methods and cell lines, and is crucial information in determining their suitabilityfor any given application. However, such assessments are generally obtained by subjective evaluation, and in the few instances where quantitative measures have been used, classification was based on ad-hoc criteria using only a few parameters. To significantly improve the accuracy and reproducibility of the phenotypic analysis, we will utilize the entire action potential shape and employ advanced image analysis and machine learning methods in an automated computational algorithm. This algorithm will be used to classify the electrophysiological phenotypes of human embryonic stem cell-derived cardiomyocytes (hESC-CMs) grown in culture, and optimized by using molecular identifiers. The specific aims of the project are to: (1 obtain archetypal electrophysiological datasets of human ventricular, atrial and nodal cells, and (2) use image analysis and machine learning methods to classify hESC-CM phenotypes. To achieve these aims, we will use computational models of the 3 major subtypes of the heart as reference points for our classification algorithm, multi-site optical measurements of action potentials, molecular and pharmacological indicators of subtype, and metamorphosis distance as a quantitative measure of the dissimilarity between action potentials. In summary, this project will result in a standardized, objective and automated computational method to classify the relative amounts of the 3 major electrophysiological phenotypes in populations of hESC-CMs, as well as the development of an action potential ontology.